As we close in on the end of 2018, it’s worth looking back at the year in AI. Yes, we’re most likely still in a hype phase for AI (although well past the peak, with practical AI now a reality in many businesses), yet 2018 did witness significant advancements in the market. Here's what we've seen.
Businesses are now far more specific in articulating AI requirements; buyer knowledge and fluency in the technology have refined substantially; and critically, companies are creating line items in their budget for AI projects. In the recent past, this spending tended to fall under “innovation”, typically in the form of pilots. The difference at the end of 2018 is the demand for specific solutions and outcomes, such as “process automation in customer service”.
This maturation is visible across many business functions and disciplines, but is particularly noteworthy in traditional cost centers like customer service - the department which also has the opportunity to most significantly impact customer loyalty, propensity to purchase, and likelihood to promote. Therefore, it is increasingly likely that if you are a customer service leader, you have been, or are about to be, asked about your plans and strategies for AI, robotics, chatbots and so on, as well as how are you going to roll them out.
With growing competition for customer attention, as well as the scramble to demonstrate the most tech-forward and intuitive experience, it’s critical to prepare your management team and the whole of your business for the onset of productized, practical AI. As you bring AI in-house, you’ll need to consider a few things.
First of all, you’ll need to convince management that your strategy is sound, and that you’ve selected the right partner to power it. Document the main pain points you face in their order of priority, their order of magnitude, and why these pain points matter based on business objectives. If you can demonstrate that a solution provider can truly solve these problems, and you’ve rigorously assessed the alternatives, the value will be clear to decision-makers.
Next, it’s important to illustrate what other resources and costs are associated in order to realize this strategy, and how to measure success. Measurement should be tied to the core business objectives, or at a minimum, to KPIs that feed into these objectives. Management will want to be kept abreast of period developments and improvements.
Furthermore, AI is less of a black box than ever before. You should be able to articulate what resources you need to onboard and set up the new technology, and which specific individuals, teams or business units are required for the ongoing deployment, maintenance and improvement of the technology.
These core tenets will provide the foundation as you plan for 2019 and beyond, as the rising tide of practical AI steadily raises all companies to greater productivity, time savings and human experience.